Our earlier approach to analyzing case-parents data for gene-environment interaction leads to valid inference for a causative SNP under two crucial assumptions. The first assumption is that, conditional on parents'genotypes, the genotype distributions of children reflect Mendelian assortment. The second is that, conditional on parents'genotypes, a child's genotype and exposure are independent. We have also studied methods for testing multiple SNP markers simultaneously and developed at testing method call TRIMM (triad multi-marker) that is resistant to bias from genetic population structure. We have recently published a manuscript that extended this work on multiple markers to examine haplotype-by-environment interactions through a procedure that we call GEI-TRIMM. Our procedure assumes that haplotypes under study have no influence on propensity to exposure and relies on the insight that, under a no-interaction null hypothesis (multiplicative scale), transmission of a causative haplotype from parents to affected offspring might show distortion from Mendelian proportions but should be independent of exposure. Simulations showed that our proposed test respects the nominal Type I error rate and provides good power under a variety of scenarios. Our procedure offers desirable features: no need for haplotype estimation, validity under unspecified genetic main effects, tolerance to Hardy-Weinberg disequilibrium and exposure-related population stratification, ability to handle missing genotypes and a relatively large number of SNPs. Though potentially informative, studying the joint effects of multiple SNP markers and environmental exposures is challenging because population structure that involves both genes and exposures can bias simple analyses. Recently, we have been considering a study design that involves one affected and one unaffected offspring and their parents. We call this structure a tetrad. Our proposal is to genotype the affected offspring and the parents and to collect exposure information from both offspring under that idea that we could test genetic and gene-environment interaction effects using the embedded case-parent-triad design and we could study exposure using the embedded sibling-pair design. In studying this design, we learned that previously proposed family-based tests of gene-environment interaction can be biased when subpopulations differ in both allele frequency and exposure prevalence (unless the SNP under study happens to be causative itself and not in linkage disequilibrium with another causal SNP, an unlikely circumstance). This finding was both surprising and troubling, as researchers had previously believed that family-based studies of gene-environment interaction would robust to bias from population structure even when studying markers. We have proposed a method that can avoid this problem when the exposure under study is dichotomous. A manuscript describing our findings is under review. (see Z01 ES040007 BB;PI Clare Weinberg.) We have conducted a crossover dietary study looking for differences in DNA mutagenicity associated with high-temperature alone vs. either high-temperature fried meat plus certain dietary supplements thought to inhibit mutagenicity or low-temperature fried meat. The biomarkers monitored include Comet assay assessment of DNA damage to colon epithelial cells, and plate-incorporation assay assessment of mutagenicity in urine and in stool. Our results indicated that meat cooked at high temperature increased mutagenicity in urine and feces and that consumption of yogurt, cruciferous vegetables, and chlorphyllin together reduced urinary and fecal mutagenicity as well as colorectal cell DNA damage. Although increased urinary mutagenicity following consumption of highly fried meat is well established, our study is the first to concurrently measure both fecal mutagenicity and DNA damage in colon epithelium and to demonstrate that dietary antimutagens can alter these characteristics. (see Z01 ES49032;PI Jack Taylor, EB.)